Why Won't Consumers Adopt M-Commerce? An Exploratory ...j.pelet.free.fr/publications/mobile/Why...

Why Won't ConsumersAdopt M-Commerce?An Exploratory Study

Pruthikrai MahatanankoonJoaquinVila-Ruiz

ABSTRACT. The adoption of mobile applications in the United Statesis still far from reaching its full potential. Numerous applications are be-ing implemented, but consumers' expectations seem to surpass its prac-tical use. A series of surveys are used to examine possible barriers thathinder the adoption of mobile commerce applications. Through ex-ploratory factor analysis, the results identify five major factors thatimpede the applicability of m-commerce: unawareness, device ineffi-ciency, conventional transactions, interoperability, and personaliza-tion needs. This study also proposes possible remedies to overcomesuch barriers. doi;10.1080/15332860802086367 [Article copies available for a feefrom The Haworth Document Delivery Service: 1-800-HAWORTH. E-mail ad-dress: <[email protected]> Website: <http:/Avww.HaworthPress.com> © 2007 by The Haworth Press. All rights reserved]

Pruthikrai Mahatanankoon is Assistant Professor of Information Systems, School ofInfonnation Technology, Illinois State University, Normal, IL 61790 (E-mail: pmahata®ilstu.edu).

Joaquin Vila-Ruiz is Professor of Computer Science/Information Systems, Schoolof Information Technology, Illinois State University, Normal, IL 61790 (E-mail: javila®ilstu.edu).

An abbreviated version, entitled "Exploring the Barriers of Mobile CommerceAdoption," was presented at the 37th Annual Decision Science Institute Conference, SanAntonio, Texas.

The authors would like to thank Mr. Juan Garcia for his initial involvement in thisresearch.

Joumal of Intemet Commerce, Vol. 6(4) 2007Available online at http://jicom.haworthpress.com© 2007 by The Haworth Press. All rights reserved.

doi:10.1080/15332860802086367 113

114 JOURNAL OF INTERNET COMMERCE

KEYWORDS. Mobile commerce, m-commerce, information technol-ogy adoption, exploratory factor analysis, consumer behavior, e-com-merce

INTRODUCTION

In the late 199O's, m-commerce (or mobile commerce) emerged asa technology that could radically affect the electronic commerce in-dustry by putting both voice and data transfers in consumers' hands.Driven by sophisticated telecommunication infrastructure and so-phisticated mobile devices, m-commerce facilitates the processes ofbuying and selling goods and services through wireless handheld de-vices. Some examples of m-commerce transactions include: buyingdigital content on the Internet, conducting banking services or tradingstocks, issuing electronic payment, tracking the location of products/services/people, etc. M-commerce will be a vital catalyst to existingelectronic commerce and has the potential to alter our lifestyles (Mathewet al., 2004).

While the telecommunication industry is continuously improvingthe transfer speed of second- and third-generation wireless networksthat will promise faster connection speeds, wireless data transfer ac-counts for only 14percentof the total wireless revenues. Accordingto the International Association for the Wireless Telecommunica-tions Industry (CTIA), the number of wireless subscribers in theUnited States was 233 millions or over 76% of the total popula-tion.' However, data from M:Metrics, Inc., suggests that only ap-proximately 13 percent of US consumers purchase digital content viatheir mobile devices (i.e., ringtones, wallpaper, or screensavers).Another study finds that only 38 percent of consumers intend to en-gage in mobile transactions^ Other promising applications, such asissuing electronic payment, electronic fund transfers, buying e-tick-ets, are still in their early phases of adoption. Numerous applicationsare currently being tested and implemented but the technological ex-pectations seem to surpass its practical use. The sluggish adoption ofmobile applications in the United States sends a clear signal thatm-commerce is still far from reaching its full potential. The objectiveof this study is to investigate what factors may impede m-commerceadoption.

Pruthikrai Mahatanankoon and Joaquin Vila-Ruiz 115

BACKGROUND

The lethargic acceptance of m-commerce is influenced in part by alack of customer demand (Zhang, Yuan & Archer, 2003). User apa-thy towards wireless data services is believed to be one of the mainfactors delaying m-commerce implementation. Consumers gener-ally rely on voice-based, emergency-based, and location-based ser-vices (Mahatanankoon, Wen and Lim, 2005). Sarker and Wells (2003)find that users' positive experience with mobile usage (i.e., functional,psychological, and relational outcomes) influence adoption decisionsand behaviors. Bouwman, Carlsson, Molina-Castillo and Walden (2007)further suggest the importance of physical, cognitive, security, andeconomic factors when delivering "bundled" mobile services to con-sumers. Unfortunately, most consumers are not totally convinced thatbuying something from their mobile devices would be a satisfactoryexperience. High subscription fees and sluggish download speed arecritical barriers to m-commerce success (Samtani, Leow, Lim &Goh, 2003). Other technical factors that can impact m-commerceadoption include user interface limitations, slow network connections,information security, or even the threat of government regulations (Wen& Mahatanankoon, 2004).

Much of the information technology adoption research, includingm-commerce adoption, relies on the Davis' (1989) Technology Ac-ceptance Model (TAM). TAM proposes that the acceptance of anytechnology is determined by two main influencing factors: perceivedusefulness and perceived ease of use. These factors are believed to havea direct impact on attitude toward using the technology. Hung, Ku &Chang (2003) use TAM and the Theory of Reasoned Action (TRA) toidentify critical factors affecting WAP services adoption. Pagani (2004)finds that perceived usefulness, ease of use, price, and speed are the ma-jor determinants for adoption of mobile services. Fundamentally, TAMconfirms that perceived usefulness and perceived ease of use are themajor determinants of attitude and usage intention.

Perceived ease of use is notably related to the ergonomic and user in-terface of mobile devices. Interface design has become another factorfrequently mentioned as having a negative effect on m-commerceadoption. For example. Lee and Benbasat (2003) recommend themulti-tasking nature of device usage as well as users' limited attentionas two of the many essential factors affecting m-commerce adoption.The limitations of screen size/resolution and cumbersome input mech-anisms hinder customers' shopping experience and lead to a major


setback in many m-commerce applications (Venkatesh, Ramesh &Massey, 2003).

TAM has been extended to predict mobile device usage in various ar-rangements. Brunner and Kumar (2005) suggest that perceived enjoy-ment may help explain consumer acceptance of handheld Internetdevices better than the original TAM constructs alone. Perceived enjoy-ment, usefulness, and expressiveness influence the intention to use mo-bile services (Nysveen, Pedersen & Thorbjornsen, 2005). Lu, Yu, Liuand Yao (2003) observe that complexity, facilitating conditions, socialinfluences, and trust factors influence user acceptance of Internet viamobile devices. Luarn and Lin (2005) also extend TAM by adding trust,self-efficacy, and transaction cost as determinants of usage intention.Furthermore, systems quality and social influence also contribute to theadoption of mobile services (Kleijnen & Wetzels, 2004). Lee, Lee andKim (2004) use Task-Technology Fit (TTF) model to evaluate how userperformance is affected by small mobile devices.

Although TAM has been used in many IT adoption studies, a prioriassumption is less useful in exploring other m-commerce barriers. Inother words, the determinants of TAM and its extensions do not providea clear, exhibitory answer to the factors that impede the widespreadadoption of m-commerce. Tarasewich (2003) proposes a context modelthat takes into consideration three main factors of adoption, which re-quire an ensemble view of several intricate components. In the particu-lar case of m-commerce, it is the point where network, devices, users,interface, and all other components work together with the objective ofserving users' transactional needs. Therefore, it is essential to define theconsumers' perceived barriers before we extensively devote ourselvesto develop a variety of mobile applications. Our study seeks to exploreother consumer-based factors that have not been previously examinedby researchers and practitioners and to answer the question, "What arethe possible barriers to consumer-based m-commerce adoption?"

METHOD

To further examine the potential barriers to m-commerce adoption,the study relied on exploratory data collection and analysis (Bordens &Abbott, 2005). Our research design intended to classify and identify po-tentially underlying adoption barriers and to examine relationships be-tween these variables. Thus, three research phases were conducted. Inthe first phase, we conducted two brainstorming sessions and generated


an elaborate list of consumer-based m-commerce adoption barriers.Based on this list, a pilot study was conducted as the second phase to re-duce any overlapping factors. We sent the final questionnaire items toour targeted population. In our last phase, exploratory and confirmatoryfactor analyses were used to verify the final dimension ofthese factors.

Phase 1-Brainstorming and Item Generation

The goals ofthe brainstorming sessions were (1) to encourage par-ticipation and creative thinking regarding usage of mobile devices,and (2) to capture consumer perception and opinions about m-com-merce activities. In order to generate a list of potential barriers hin-dering the adoption of m-commerce, twenty-one students from twodifferent classes participated in two separate brainstorming sessions.The participants majored in information systems, computer science, ortelecommunications management.

First, the participants were provided with the definition of m-com-merce to avoid possible misinterpretations about the extent of the tech-nology and its applications. They were told that the study focused ontransactional applications, which encompass the purchasing of physicalgoods and services. These types of applications involve monetary trans-actions in which real commerce is taking place. Then, they were given30 minutes to answer on paper several open-ended questions about dif-ferent aspects of m-commerce, such as "What do you consider are themain factors hindering the adoption of m-commerce?" After compilingand processing the feedback, a list of 41 barriers was generated.

Phase 2-Pilot Study and Data Collection

A pilot web-based survey was conducted using a different set of un-dergraduate and graduate students with similar backgrounds as the par-ticipants in the brainstorming session. The main goals behind the pilotstudy were to verify the readability and accuracy of the questionnaireitems and to ensure a certain degree of validity of the items. The re-spondents were guaranteed anonymity and confidentiality of their re-sponses. They were also given a bonus credit for their willingness toparticipate in the study. Forty-seven students responded to the initialquestionnaire. As a result, our exploratory factor analysis eliminated 17items that had insignificant factor loading, cross loading, or ambiguousinterpretations. Table 1 shows the final 24 items used in the study.


TABLE 1. Barriers to M-Commerce Adoption

List of Barriers to M-commercc AdoptionBill am unaware of ray mobile device's m-commerce eapabilities.B2:1 lack knowledge ofthe pi-icing scheme of mobile commerce transactions.B3:1 am unaware ofthe existing mobile commerce applications.B4: My mobile service carrier (e.g., Verizon, Sprint, Cingular, etc.) does not providemobile commerce applications.B5: My Internet vendors (e.g., Amazon.com, BestBuy.coni, etc.) do not offer the mobiletransaction services.B6: Using my computer to purchase products/services online is faster than using mymobile device for the same activities.B7; My mobile carriers (e.g., Verizon, Sprint, Cingular, etc.) do not provide anyadditional services other than simple Internet access.B8: The manufacturers do not develop mobile commerce applications for my mobiledevice.B9; It is costly to add additional mobile commerce services to my subscription plan.BIO: I am used to physical fornis of payments.Bl 1:1 am impatient with my m-commerce applications.B12:1 prefer to engage in face-to-face interaction when buying products/services.Bl 3:1 prefer to buy products/services through computers.B14:1 need to be able to customize my m-commerce activities.B15: My mobile device is cumbersome for m-commerce activities.B16:1 thitik it is too time consuming to perform m-conunerce activities.B17:1 need to personalize my m-commerce activities.B18: The ergonomic of my mobile devices hinders my acceptance of m-coninierceapplications.B19: The lack of telecommunications standards hinders my acceptance of m-commerceapplications.B20: The reliability of my mobile ser\'ice carrier (e.g.. Verizon, Sprint, Cingular, etc.)hinders the widespread acceptance of m-comraerce.B21: My mobile device can be customized/personalized to reflect my m-commerceactivities.B22:1 prefer an electronic form of payment via my mobile device.B2.3: The roaming capability of my mobile device hinders my m-commerce activities.B24: The interoperability of different mobile sendee carriers (e.g., Verizon, Sprint,Cingular, etc.) hinders my m-commerce activities.

To collect data for this study, mass e-mails containing a link to ourweb-based survey were sent out to approximately 19,000 students en-rolled at a large state university in the Midwest. These students wereasked to respond to the final 24 items on the basis of their usage and per-ceptions using a 7-point Likert scale (1 = strongly disagree to 7 =strongly agree). An electronic gaming device was offered as an incen-tive to the participants. We eliminated all respondents who did not ownan Internet-enabled mobile phone as well as respondents who had other


mobile devices (i.e., PDA and PocketPC). There were 215 respondentsused in this study. The sample was composed of more females thanmales (66% vs. 34%). A large percentage (60%) of the respondentswere 21-25 years of age, while a smaller percentage (32%) of the re-spondents had ages ranging from 26-40 years old. Table 2 shows anoverall measure for each demographic.

Phase 3-Exploratory and Confirmatory Factor Analyses

Exploratory factor analysis (EFA) was conducted to identify thepreliminary factors hindering the adoption of m-commerce. Since wehad no idea how to cluster these items, the goal of EEA was to narrow alarger set of variables into a more manageable set of factors (Tabachnick& Fidell, 2001). Based on a sample size of 215 with a significant level of.05, items with less than a 0.40 factor loading on any component weredropped. We also eliminated items with significant cross loadings and/orlow communalities. The initial principle components with varimax rota-tion produced a five-factor solution based on 13 items.

To establish the construct reliability among these factors, the samplewas randomly split into two groups (Igbaria & Baroudi, 1993): dataset 1(n = 108) and dataset 2 (n = 107). Table 2 indicates that the two samplesdo not differ in terms of age, gender, and education. Confirmatory fac-tor analysis (CFA), a pre-specification of the interrelationships among

TABLE 2. Characteristics of Random Samples

DemographicVariables

Age21-2526-3031-4041-50

GenderMaleFemale

EducationUndergraduate

Graduate

Dataset 1N=]08

60.2%19.4%13.0%7.4%

38.0%62.0%

70.4%29.6%

Dataset 2N=IO7

59.8%20.6%10.3%9.3%

29.9%70.1%

60.8%39.2%

/ = 0.609, p = 0.894

/ = 1.556, ;j = 0.212

/ = 2.205, ;?-0.138


variables, was used to validate the five-factor solution and to determineseveral fit measures. The results showed that they supported our factorsin general, suggesting an acceptable fit between our data and the under-lying adoption barriers. The five barriers accounted for 73.7 and 76.9percent of the total variation among items for dataset 1 and dataset 2, re-spectively. Composite reliability (a) of each construct fell within an ac-ceptable range (.673 - .873) for this type of exploratory study. Tables 3aand 3b display the final loadings of each factor, composite reliability,and the overall fit indices.

TABLE 3A. Results from Factor Analysis of Dataset 1 (NM-commerce Adoption Barriers

Bl: I am uuaware of my mobile device's m-cotnmerce capabilities.B2: 1 lack knowledge of the pricing scheme ofmobile cotnmeice transactions.B3:1 am unaware of the existing mobile commerceapplications.B6: Using my computer to purchaseproducts/services online is faster than using mymobile device for the same activities.B15: My mobile device is cumbersome for m-cominerce activities.B16:1 think it is too time consuming to ])erfomi m-commerce activities.BIO: 1 am used to physical forms of payments.

B12: 1 prefer to engage in faee-to-face interactionwhen buying products/services.B19: The lack of telecommunications standardshinders my acceptance of m-commerce applications.B23: The roaming capability of my mobile devicehinders my m-coinmerce activities.B24: The interoperability of different mobile seivicecarriers (e.g., Verizon, Sprint, etc.) hinders my m-commerce activities.BI4: T need to be able to customize my m-eommerceactivities.B17: 1 need to personalize my m-commerceactivities.

MeanS.D.

Composite Reliability (a)Fit Indexes of First-Order CFA

FI.771

.915

.870

3.991.71.871

F2

.612

.847

.789

4.731.21.673

AGFI=.

F3

.907

.908

4.291.73.826

= 108)F4

.595

.872

.860

3.521.27.711

291,GFI=.9O7•i45, CF1=.964,

RMSEA=.O52

""FS"

.773

.894

4.191.09.677


TABLE 3B. Results from Factor Analysis of Dataset 2 (N =

M-commerce Adoption BarriersBl: I am unaware of my mobile device's m-commeree capabilities.B2:1 lack knowledge of the pricing scheme ofmobile commerce transactions.B3:1 am unaware of the existing mobile commerceapplications.B6: Using my computer to purchaseproducts/services online is faster than using mymobile device for the same activities.B15: My mobile device is cumbersome for m-commerce activities.B16:1 think it is too time consuming to perform m-commeree activities.BIO: I am used to physical forms of payments.

B!2:1 prefer to engage in face-to-face interactionwhen buying products/services.B19: The lack of telecommunications standardshinders my acceptance of m-commeree applications.B23: The roaming capability of my mobile devicehinders my m-commerce activities.B24: The interoperability of different mobile servicecarriers (e.g., Verizon, Sprint, etc.) hinders my m-commeree activities.B14:1 need to be able to customize my m-commerceactivities.B17:1 need to personalize my m-commerceactivities.

MeanS.D.

Composite Reliability (a)Fit Indexes of First-Order CFA

FI.871

.851

.846

4.271.78.873

X

F2

.612

.785

.860

4.731.20.676

F3

.891

.899

4.261.83.800

107)

F4

.489

.850

.849

3.571.15.727

/̂(//•=1.3O7,GF1=.9O7ACTF1=.846, CF1=965,

RMSEA=.O38

FS

.913

.860

4.041.21.794

To further confirm that these factors are indeed important barriers tom-commerce adoption, we tested for nomological validity by examin-ing their relationships with m-commerce usage behaviors (MCU). M-com-merce usage behaviors were measured using a 5-point Likert scale from"Strongly Disagree" to "Strongly Agree." Respondents were asked "Iuse my mobile device to buy products and services," "I use my mobiledevice for a variety of m-commerce applications," and "Overall, I en-


gage in mobile commerce a lot." Composite reliability (a) of m-com-merce usage behaviors (MCU) were .927 and .959 for dataset 1 anddataset 2, respectively. The correlations between the explored barriersand m-commerce usage behaviors (MCU) are shown in Table 4. The re-sults of dataset 2 showed three negative correlations between MCU andFactor 1 (consumer unawareness). Factor 2 (device inefficiency), andFactor 3 (demand for conventional business transactions); while the re-sults of dataset 1 indicated that only Factor 1 (consumer unawareness)negatively influenced MCU. All other factors either had insignificantpositive or negative minimal correlations with MCU. A negative corre-lation between each potential barrier and MCU signified a major imped-iment of mobile commerce activities.

RESULTS AND IMPLICATIONS

Our results suggest five m-commerce adoption barriers that provide acomplementary consumer-based perspective to previously identifiedimpediments (Sarker & Wells, 2003; Zhang, Yuan & Archer, 2003;Wen & Mahatanankoon, 2004; Bouwman, Carlsson, Molina-Castillo &Walden, 2007). Table 5 summarizes five m-commerce adoption barriers.

Based on the mean value of each factor (Tables 3a and 3b), consum-ers consider "device inefficiency" and "demand for physical transac-

TABLE 4. Correlation with M-Commerce Usage (MCU)

Factors FI F2 F3 F4 F5 MCUDataset 1

Fi 1.000F2F3F4F5

MCUDataset 2

Fl 1.000F2F3F4F5

MCU

.279**1.000

.1591.000

.257**

.1491.000

.285**

.0411.000

.279**

.006

.0971.000

.390**

.244*

.1041.000

-.077.294**

-.127-.0471.000

-.033.315**.055.249**

1.000

-.329**-.173-.017.007.140

1.000

-.317**-.222*-.220*-.133

.0761.000

* Correlation is significant at Ihe 0.05 level (2-tailed).' • Correlalion Is significant at the 0.01 levet (2-tailed).

Pruthikrai Mahatanankoon and Joaquin Vila-Ruiz

TABLE 5. Summary of M-Commerce Adoption Barriers

123

Factors

Consumer Unawareness

Device Inefficiency

Demand for ConventionalBusiness Transactions

Interoperability Concerns

Personalization Needs

Definitions

The lack of general m-commerceawareness and.'or pricing schemeawareness.The inefficiency or awkwardness ofthe devices used to engage in mobiletransactions.The concerns for trust and face-to-faceinteraction when buying productsand/or ser\'ices.The roaming capability andinteroperability of the different m-commerce services.The importance of customizationand'or personalization of m-commerceactivities.

tions" to be the most important factors. "Interoperability" has the lowestimpact on the m-commerce adoption. We will discuss each factor indetail.

Consumer unawareness occurs when consumers are not aware ofwhat mobile applications are available to them. Two levels of unaware-ness need to be fully addressed in order overcome such a banier: (1) gen-eral m-commerce and (2) pricing scheme. Consumers often perceivem-commerce as surfing the Internet, checking sports, or viewing weatherinformation. Some may be aware of m-commerce applications but donot know how to install them on their devices. Consumer self-efficacygenerally plays a significant role when it comes to mobile gadgets.Consumers with prior exposure to other mobile devices are found toencounter difficulties in switching from their existing technologicalframes (Orlikowski & Gash, 1994). Furthermore, m-commerce market-ing relies on word-of-mouth and other intricate social factors. For ex-ample, a consumer will utilize mobile applications if his/her friends areactive mobile users (Lu, Yu, Liu & Yao, 2003; Kleijnen & Wetzels,2004).

Pricing scheme unawareness can be detrimental if consumers do notknow how they are going to be charged for mobile transactions. In orderto increase m-commerce awareness, wireless carriers should be lucid


about their transactional and subscription fees, as well as, designing aneffective marketing campaign to target potential customers regardinginnovative m-commerce services (Luarn & Lin, 2005).

Device inefficiency continues to be a problem for m-commerce appli-cations. M-commerce could not fulfill its potential without widespreadproliferation of wireless devices and related applications. But everyextra input that a user needs to make in navigation with a mobile de-vice reduces the possibility of a transaction by 50% (Clarke III, 2001).A visit to Barnes & Noble's WAP site to enter credit card number, ad-dress, and shipping information requires more than 100 keystrokes(Swartz, 2001). Furthermore, adding "cool" features to a mobile devicecan make it less convenient to use (Bruner & Kumar, 2005). User inter-faces need to be designed to support users' limited attention. In order toaccomplish a good design, it is imperative that manufactures are awareof the situations in which consumers conduct their mobile tasks (Lee &Benbasat, 2003). Recently, speech recognition has become increasinglypopular to facilitating the interaction with wireless mobile devices (Fan,Saliba, Kendall & Newmarch, 2005). It is possible that in the future,with the advent of wearable computers, these technologies will reducethe inefficiency of mobile devices. Future research can explore the con-textual usability factors of m-commerce applications in various socialsettings.

Demands for conventional business transactions relate to psycholog-ical attributes rather than technological ones. The main challenge form-commerce is to ensure consumers' trust by making them feelcomfortable with wireless transactions. In many cases, consumersdo not like the idea of entering personal information into their mobilephones, fearing loss/theft of their devices or exposing their credit cardinformation to public wireless networks. The advantage of small mobiledevices, such as portability and small size, become the biggest disad-vantage when they are lost or stolen (Tarasewich, 2003).

Moreover, the idea of not dealing with someone face-to-face, ornot being able to touch the merchandise may sound unattractive tomany. Based on our findings, only specific types of products/services (i.e., digital and information products, basic daily commodi-ties, point-of-sale) are suitable for mobile transactions. Remember thatthe m-commerce shopping experience is not intended to replace elec-tronic commerce or physical stores. Electronic commerce custonriersmay decide to buy products from a trusted vendor just by looking at itsreliability and reviews, but for m-commerce consumers, this functional-ity still remains a challenge.


Interoperability concerns relate to the inability of using the same mo-bile device anywhere in the world. A simple example of the lack ofinteroperability is when some carriers offer text message confirmationdelivery within their own network but not when the message recipienthas a different carrier. M-commerce services are hindered by a varietyof network communication standards such as 2G, 2.5G, 3G, 3.5G. Even3G, which was supposed to be a single, unified, worldwide standard,has been mainly split into UMTS/HSDPA, and EV-DO. Applicationsare developed based on device characteristics and carrier standards,which create interoperability problems between applications providers.Efforts are being conducted to ensure interoperability. A big advancetoward interoperability occurred in July 2004 when The World WideWeb Consortium (W3C) and the Open Mobile Alliance (OMA) an-nounced a Memorandum of Understanding (MoU) that would enableboth organizations to collaborate on specifications for mobile access tothe Web.

Personalization needs are context-specific services customized toeach individual. M-commerce services must be personalized and tai-lored to each consumer based on his/her profile, location and need.These operations range from customized ring tone recommendationsto location-based services (Ho & Kwok, 2003). One of the most im-portant aspects to consider is that mobile users demand packets of 'hy-per-personalized' information, not scaled-down versions of genericinformation (Barnes & Scornavacca, 2004).

Context relevance is the capability of sending the appropriate mes-sage at the appropriate time and place (Jelassi & Enders, 2004). Imple-menting a context relevant application can increase perceived usefulnessof mobile services. Through a small user interface, consumers will havethe ability to effectively filter the information received. Personalizationcan help towards solving the problem of small screen display (Ho &Kwok, 2003). However, the main obstacle toward personalization isprivacy and security, which can negatively impact the prevalent use ofsuch applications. So far, there has not been an approach focusing spe-cifically on the wireless/mobile user (Panayiotou & Samaras, 2004).

M-commerce has an enormous potential as a new inspiring technol-ogy even though it is still in its early stages of development. As its hypesubsides, the industry can effectively search for solutions to overcomethese barriers. Our findings may not cover all the potential factors im-peding consumer-based m-commerce, but it will open the door forfurther research.


LIMITATIONS

There are several limitations to this study. First, the study used a conve-nience sample drawn from a student population. With a very low responserate, practitioners must take precautions when applying the results to gen-eral consumers. Nevertheless, M:Metrics, Inc., the mobile market author-ity, finds that owners of complex mobile devices use them for productivityand non-productivity applications, such as viewing video, playing games,listening to music, answering personal e-mails, etc. It also suggests that"youthfulness is a key characteristic of this demographic," where one-thirdof mobile consumers in the US are 18-34 years of age.^ Second, the resultsof this study also ruled out cultural, economical, geographical, and socialfactors that could predict m-commerce adoption behaviors. For example,cultural differences can potentially play a significant role in m-commerceadoption (Harris, Rettie and Kwan, 2005). Sarker and Wells (2003) suggestthat cultural origin influences individuals' patterns of mobile technologyusage. Carlson, Kahn and Rowe (1999) attribute cultural differences to thedisparity of mobile devices adoption between the United States and France.Finally, economic and infrastructure issues can influence the rate of m-commerce adoption (Fraunholz & Unnithan, 2004). These macro-leveldrivers and inhibitors can be examined in future research.

CONCLUSION

This study offers guidelines to researchers and practitioners of whatcould be done to m-commerce adoption from consumers' perspective.There are consumer-based potential barriers that have not been taken intoconsideration in prior studies, such as demands for conventional businesstransactions, consumer unawareness, and customization. Researcherscan further investigate and examine these barriers to expand the existingknowledge and theories. Practitioners and the electronic commerce in-dustry can promote better marketing strategies and develop innovativemobile applications that will potentially breakdown these barriers.

NOTES

1. http.V/www.ctia.org, as of December 2006.2. http://www.m-travel.com/news/2004/02/survey_finds_50.html3. hUp://www.mmetrics.coin/press/articles/20070514-hispanic.pdf


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